11 Feb 2022

Delayed reporting is a significant problem for effective pandemic surveillance and decision-making. In the absence of timely data, statistical models which account for delays can be adopted to nowcast and forecast cases or deaths. I will first explain four key sources of systematic and random variability in available data for daily COVID-19 deaths in English hospitals. Then, I will present a general hierarchical approach which I claim can appropriately separate and capture this variability better than competing approaches. I will back up my claim with theoretical arguments and with results from a rolling prediction experiment imitating real-world use of these methods throughout the second wave of COVID in England.